Identifying entities using a deep-learning model

ABSTRACT

In one embodiment, a method includes retrieving a first vector representation of a first entity, with which a user has interacted, and a second vector representation of a second entity, with which the user has not interacted. The first and second vector representations are determined using an initial deep-learning model. A first similarity score is computed between a vector representation of the user and the first vector representation, and a second similarity score is computed between the vector representation of the user and the second vector representation. The second vector representation is updated if the second similarity score is greater than the first similarity score using the initial deep-learning model. An updated deep-learning model is generated based on the initial deep-learning model and on the updated second vector representation.

PRIORITY

This application is a continuation under 35 U.S.C. § 120 of U.S. patentapplication Ser. No. 14/984,956, filed 30 Dec. 2015, which isincorporated herein by reference.

TECHNICAL FIELD

This disclosure generally relates to training a deep-learning model.

BACKGROUND

Deep-learning is a type of machine learning that may involve training amodel in a supervised or unsupervised setting. Deep-learning models maybe trained to learn representations of data. As an example and not byway of limitation, a deep-learning model may represent data as vectorsof intensity values. Deep-learning models may be used in classificationof data. Classification may involve determining which of a set ofcategories a data point belongs to by training the deep-learning model.

SUMMARY OF PARTICULAR EMBODIMENTS

In particular embodiments, a social-networking system may use adeep-learning model to predict relevant entities for a user. Thedeep-learning model may be trained to map entities to vectorrepresentations. A vector representation of a user may be determinedbased on a set of entities with which the user has interacted in asocial-networking system. In particular embodiments, a target entity maybe selected and removed from the set of entities with which the user hasinteracted. The vector representations of the remaining entities in theset of entities may be combined to yield a vector representation of theuser. The deep-learning model may be trained using the target entity asa supervisory signal. Vector representations of entities with which theuser has not interacted may be determined using the deep-learning model.An embedding of an entity or the user may be determined based on therespective vector representation, which may correspond to coordinates ofa point in a multi-dimensional embedding space. The embedding may be arepresentation of an entity or user in the embedding space. Theembedding space may include one or more user embeddings and a pluralityof embeddings of entities. These user and entity embeddings may be usedto accomplish any number of suitable tasks. As an example and not by wayof limitation, the social-networking system may employ a searchalgorithm to identify one or more entities—with which the user has notinteracted—that have embeddings proximate to the user embedding in theembedding space. The social-networking system may determine that theidentified entities are relevant to the user.

In particular embodiments, the identified entities may be sent asrecommendations to a client system of the user. The identified entitiesmay be displayed to the user as suggestions on an interface of anapplication running on the client system (e.g., a messaging platform oran application associated with the social-networking system). The usermay select one or more entities from the set of identified entities, andthe selected entity may, as an example and not by way of limitation,link the user to another page in an application hosted by thesocial-networking system.

The embodiments disclosed herein are only examples, and the scope ofthis disclosure is not limited to them. Particular embodiments mayinclude all, some, or none of the components, elements, features,functions, operations, or steps of the embodiments disclosed herein.Embodiments according to the invention are in particular disclosed inthe attached claims directed to a method, a storage medium, a system anda computer program product, wherein any feature mentioned in one claimcategory, e.g., method, can be claimed in another claim category, e.g.,system, as well. The dependencies or references back in the attachedclaims are chosen for formal reasons only. However any subject matterresulting from a deliberate reference back to any previous claims (inparticular multiple dependencies) can be claimed as well, so that anycombination of claims and the features thereof are disclosed and can beclaimed regardless of the dependencies chosen in the attached claims.The subject-matter which can be claimed comprises not only thecombinations of features as set out in the attached claims but also anyother combination of features in the claims, wherein each featurementioned in the claims can be combined with any other feature orcombination of other features in the claims. Furthermore, any of theembodiments and features described or depicted herein can be claimed ina separate claim and/or in any combination with any embodiment orfeature described or depicted herein or with any of the features of theattached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example network environment associated with asocial-networking system.

FIG. 2 illustrates an example social graph.

FIG. 3 illustrates an example deep-learning model.

FIG. 4 illustrates an example dictionary mapping entities to vectorrepresentations.

FIG. 5 illustrates an example method for determining an embedding ofuser using a deep-learning model.

FIG. 6 illustrates an example method for training a deep-learning model.

FIG. 7 illustrates an example view of an embedding space.

FIG. 8 illustrates an example method for updating vector representationsof entities using a deep-learning model.

FIG. 9 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS System Overview

FIG. 1 illustrates an example network environment 100 associated with asocial-networking system. Network environment 100 includes a clientsystem 130, a social-networking system 160, and a third-party system 170connected to each other by a network 110. Although FIG. 1 illustrates aparticular arrangement of client system 130, social-networking system160, third-party system 170, and network 110, this disclosurecontemplates any suitable arrangement of client system 130,social-networking system 160, third-party system 170, and network 110.As an example and not by way of limitation, two or more of client system130, social-networking system 160, and third-party system 170 may beconnected to each other directly, bypassing network 110. As anotherexample, two or more of client system 130, social-networking system 160,and third-party system 170 may be physically or logically co-locatedwith each other in whole or in part. Moreover, although FIG. 1illustrates a particular number of client systems 130, social-networkingsystems 160, third-party systems 170, and networks 110, this disclosurecontemplates any suitable number of client systems 130,social-networking systems 160, third-party systems 170, and networks110. As an example and not by way of limitation, network environment 100may include multiple client system 130, social-networking systems 160,third-party systems 170, and networks 110.

This disclosure contemplates any suitable network 110. As an example andnot by way of limitation, one or more portions of network 110 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. Network 110 may include one or more networks110.

Links 150 may connect client system 130, social-networking system 160,and third-party system 170 to communication network 110 or to eachother. This disclosure contemplates any suitable links 150. Inparticular embodiments, one or more links 150 include one or morewireline (such as for example Digital Subscriber Line (DSL) or Data OverCable Service Interface Specification (DOCSIS)), wireless (such as forexample Wi-Fi or Worldwide Interoperability for Microwave Access(WiMAX)), or optical (such as for example Synchronous Optical Network(SONET) or Synchronous Digital Hierarchy (SDH)) links. In particularembodiments, one or more links 150 each include an ad hoc network, anintranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, aportion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 150, or a combination of two or more such links150. Links 150 need not necessarily be the same throughout networkenvironment 100. One or more first links 150 may differ in one or morerespects from one or more second links 150.

In particular embodiments, client system 130 may be an electronic deviceincluding hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by clientsystem 130. As an example and not by way of limitation, a client system130 may include a computer system such as a desktop computer, notebookor laptop computer, netbook, a tablet computer, e-book reader, GPSdevice, camera, personal digital assistant (PDA), handheld electronicdevice, cellular telephone, smartphone, augmented/virtual realitydevice, other suitable electronic device, or any suitable combinationthereof. This disclosure contemplates any suitable client systems 130. Aclient system 130 may enable a network user at client system 130 toaccess network 110. A client system 130 may enable its user tocommunicate with other users at other client systems 130.

In particular embodiments, client system 130 may include a web browser132, and may have one or more add-ons, plug-ins, or other extensions. Auser at client system 130 may enter a Uniform Resource Locator (URL) orother address directing the web browser 132 to a particular server (suchas server 162, or a server associated with a third-party system 170),and the web browser 132 may generate a Hyper Text Transfer Protocol(HTTP) request and communicate the HTTP request to server. The servermay accept the HTTP request and communicate to client system 130 one ormore Hyper Text Markup Language (HTML) files responsive to the HTTPrequest. Client system 130 may render a webpage based on the HTML filesfrom the server for presentation to the user. This disclosurecontemplates any suitable webpage files. As an example and not by way oflimitation, webpages may render from HTML files, Extensible Hyper TextMarkup Language (XHTML) files, or Extensible Markup Language (XML)files, according to particular needs. Such pages may also executescripts, combinations of markup language and scripts, and the like.Herein, reference to a webpage encompasses one or more correspondingwebpage files (which a browser may use to render the webpage) and viceversa, where appropriate.

In particular embodiments, social-networking system 160 may be anetwork-addressable computing system that can host an online socialnetwork. Social-networking system 160 may generate, store, receive, andsend social-networking data, such as, for example, user-profile data,concept-profile data, social-graph information, or other suitable datarelated to the online social network. Social-networking system 160 maybe accessed by the other components of network environment 100 eitherdirectly or via network 110. In particular embodiments,social-networking system 160 may include one or more servers 162. Eachserver 162 may be a unitary server or a distributed server spanningmultiple computers or multiple datacenters. Servers 162 may be ofvarious types, such as, for example and without limitation, web server,news server, mail server, message server, advertising server, fileserver, application server, exchange server, database server, proxyserver, another server suitable for performing functions or processesdescribed herein, or any combination thereof. In particular embodiments,each server 162 may include hardware, software, or embedded logiccomponents or a combination of two or more such components for carryingout the appropriate functionalities implemented or supported by server162. In particular embodiments, social-networking system 160 may includeone or more data stores 164. Data stores 164 may be used to storevarious types of information. In particular embodiments, the informationstored in data stores 164 may be organized according to specific datastructures. In particular embodiments, each data store 164 may be arelational, columnar, correlation, or other suitable database. Althoughthis disclosure describes or illustrates particular types of databases,this disclosure contemplates any suitable types of databases. Particularembodiments may provide interfaces that enable a client system 130, asocial-networking system 160, or a third-party system 170 to manage,retrieve, modify, add, or delete, the information stored in data store164.

In particular embodiments, social-networking system 160 may store one ormore social graphs in one or more data stores 164. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. Social-networking system 160 mayprovide users of the online social network the ability to communicateand interact with other users. In particular embodiments, users may jointhe online social network via social-networking system 160 and then addconnections (e.g., relationships) to a number of other users ofsocial-networking system 160 to whom they want to be connected. Herein,the term “friend” may refer to any other user of social-networkingsystem 160 with whom a user has formed a connection, association, orrelationship via social-networking system 160.

In particular embodiments, social-networking system 160 may provideusers with the ability to take actions on various types of items orobjects, supported by social-networking system 160. As an example andnot by way of limitation, the items and objects may include groups orsocial networks to which users of social-networking system 160 maybelong, events or calendar entries in which a user might be interested,computer-based applications that a user may use, transactions that allowusers to buy or sell items via the service, interactions withadvertisements that a user may perform, or other suitable items orobjects. A user may interact with anything that is capable of beingrepresented in social-networking system 160 or by an external system ofthird-party system 170, which is separate from social-networking system160 and coupled to social-networking system 160 via a network 110.

In particular embodiments, social-networking system 160 may be capableof linking a variety of entities. As an example and not by way oflimitation, social-networking system 160 may enable users to interactwith each other as well as receive content from third-party systems 170or other entities, or to allow users to interact with these entitiesthrough an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 170 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., with which servers may communicate. A third-party system 170 maybe operated by a different entity from an entity operatingsocial-networking system 160. In particular embodiments, however,social-networking system 160 and third-party systems 170 may operate inconjunction with each other to provide social-networking services tousers of social-networking system 160 or third-party systems 170. Inthis sense, social-networking system 160 may provide a platform, orbackbone, which other systems, such as third-party systems 170, may useto provide social-networking services and functionality to users acrossthe Internet.

In particular embodiments, a third-party system 170 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 130. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects.

In particular embodiments, social-networking system 160 also includesuser-generated content objects, which may enhance a user's interactionswith social-networking system 160. User-generated content may includeanything a user can add, upload, send, or “post” to social-networkingsystem 160. As an example and not by way of limitation, a usercommunicates posts to social-networking system 160 from a client system130. Posts may include data such as status updates or other textualdata, location information, photos, videos, links, music or othersimilar data or media. Content may also be added to social-networkingsystem 160 by a third-party through a “communication channel,” such as anewsfeed or stream.

In particular embodiments, social-networking system 160 may include avariety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, social-networking system 160 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, advertisement-targetingmodule, user-interface module, user-profile store, connection store,third-party content store, or location store. Social-networking system160 may also include suitable components such as network interfaces,security mechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments,social-networking system 160 may include one or more user-profile storesfor storing user profiles. A user profile may include, for example,biographic information, demographic information, behavioral information,social information, or other types of descriptive information, such aswork experience, educational history, hobbies or preferences, interests,affinities, or location. Interest information may include interestsrelated to one or more categories. Categories may be general orspecific. As an example and not by way of limitation, if a user “likes”an article about a brand of shoes the category may be the brand, or thegeneral category of “shoes” or “clothing.” A connection store may beused for storing connection information about users. The connectioninformation may indicate users who have similar or common workexperience, group memberships, hobbies, educational history, or are inany way related or share common attributes. The connection informationmay also include user-defined connections between different users andcontent (both internal and external). A web server may be used forlinking social-networking system 160 to one or more client systems 130or one or more third-party system 170 via network 110. The web servermay include a mail server or other messaging functionality for receivingand routing messages between social-networking system 160 and one ormore client systems 130. An API-request server may allow a third-partysystem 170 to access information from social-networking system 160 bycalling one or more APIs. An action logger may be used to receivecommunications from a web server about a user's actions on or offsocial-networking system 160. In conjunction with the action log, athird-party-content-object log may be maintained of user exposures tothird-party-content objects. A notification controller may provideinformation regarding content objects to a client system 130.Information may be pushed to a client system 130 as notifications, orinformation may be pulled from client system 130 responsive to a requestreceived from client system 130. Authorization servers may be used toenforce one or more privacy settings of the users of social-networkingsystem 160. A privacy setting of a user determines how particularinformation associated with a user can be shared. The authorizationserver may allow users to opt in to or opt out of having their actionslogged by social-networking system 160 or shared with other systems(e.g., third-party system 170), such as, for example, by settingappropriate privacy settings. Third-party-content-object stores may beused to store content objects received from third parties, such as athird-party system 170. Location stores may be used for storing locationinformation received from client systems 130 associated with users.Advertisement-pricing modules may combine social information, thecurrent time, location information, or other suitable information toprovide relevant advertisements, in the form of notifications, to auser.

Social Graphs

FIG. 2 illustrates example social graph 200. In particular embodiments,social-networking system 160 may store one or more social graphs 200 inone or more data stores. In particular embodiments, social graph 200 mayinclude multiple nodes—which may include multiple user nodes 202 ormultiple concept nodes 204—and multiple edges 206 connecting the nodes.Example social graph 200 illustrated in FIG. 2 is shown, for didacticpurposes, in a two-dimensional visual map representation. In particularembodiments, a social-networking system 160, client system 130, orthird-party system 170 may access social graph 200 and relatedsocial-graph information for suitable applications. The nodes and edgesof social graph 200 may be stored as data objects, for example, in adata store (such as a social-graph database). Such a data store mayinclude one or more searchable or queryable indexes of nodes or edges ofsocial graph 200.

In particular embodiments, a user node 202 may correspond to a user ofsocial-networking system 160. As an example and not by way oflimitation, a user may be an individual (human user), an entity (e.g.,an enterprise, business, or third-party application), or a group (e.g.,of individuals or entities) that interacts or communicates with or oversocial-networking system 160. In particular embodiments, when a userregisters for an account with social-networking system 160,social-networking system 160 may create a user node 202 corresponding tothe user, and store the user node 202 in one or more data stores. Usersand user nodes 202 described herein may, where appropriate, refer toregistered users and user nodes 202 associated with registered users. Inaddition or as an alternative, users and user nodes 202 described hereinmay, where appropriate, refer to users that have not registered withsocial-networking system 160. In particular embodiments, a user node 202may be associated with information provided by a user or informationgathered by various systems, including social-networking system 160. Asan example and not by way of limitation, a user may provide his or hername, profile picture, contact information, birth date, sex, maritalstatus, family status, employment, education background, preferences,interests, or other demographic information. In particular embodiments,a user node 202 may be associated with one or more data objectscorresponding to information associated with a user. In particularembodiments, a user node 202 may correspond to one or more webpages.

In particular embodiments, a concept node 204 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (e.g., for example, a movie theater, restaurant,landmark, or city); a website (e.g., a website associated withsocial-network system 160 or a third-party website associated with aweb-application server); an entity (e.g., a person, business, group,sports team, or celebrity); a resource (e.g., an audio file, video file,digital photo, text file, structured document, or application) which maybe located within social-networking system 160 or on an external server,such as a web-application server; real or intellectual property (e.g., asculpture, painting, movie, game, song, idea, photograph, or writtenwork); a game; an activity; an idea or theory; an object in aaugmented/virtual reality environment; another suitable concept; or twoor more such concepts. A concept node 204 may be associated withinformation of a concept provided by a user or information gathered byvarious systems, including social-networking system 160. As an exampleand not by way of limitation, information of a concept may include aname or a title; one or more images (e.g., an image of the cover page ofa book); a location (e.g., an address or a geographical location); awebsite (which may be associated with a URL); contact information (e.g.,a phone number or an email address); other suitable concept information;or any suitable combination of such information. In particularembodiments, a concept node 204 may be associated with one or more dataobjects corresponding to information associated with concept node 204.In particular embodiments, a concept node 204 may correspond to one ormore webpages.

In particular embodiments, a node in social graph 200 may represent orbe represented by a webpage (which may be referred to as a “profilepage”). Profile pages may be hosted by or accessible tosocial-networking system 160. Profile pages may also be hosted onthird-party websites associated with a third-party system 170. As anexample and not by way of limitation, a profile page corresponding to aparticular external webpage may be the particular external webpage andthe profile page may correspond to a particular concept node 204.Profile pages may be viewable by all or a selected subset of otherusers. As an example and not by way of limitation, a user node 202 mayhave a corresponding user-profile page in which the corresponding usermay add content, make declarations, or otherwise express himself orherself. As another example and not by way of limitation, a concept node204 may have a corresponding concept-profile page in which one or moreusers may add content, make declarations, or express themselves,particularly in relation to the concept corresponding to concept node204.

In particular embodiments, a concept node 204 may represent athird-party webpage or resource hosted by a third-party system 170. Thethird-party webpage or resource may include, among other elements,content, a selectable or other icon, or other inter-actable object(which may be implemented, for example, in JavaScript, AJAX, or PHPcodes) representing an action or activity. As an example and not by wayof limitation, a third-party webpage may include a selectable icon suchas “like,” “check-in,” “eat,” “recommend,” or another suitable action oractivity. A user viewing the third-party webpage may perform an actionby selecting one of the icons (e.g., “check-in”), causing a clientsystem 130 to send to social-networking system 160 a message indicatingthe user's action. In response to the message, social-networking system160 may create an edge (e.g., a check-in-type edge) between a user node202 corresponding to the user and a concept node 204 corresponding tothe third-party webpage or resource and store edge 206 in one or moredata stores.

In particular embodiments, a pair of nodes in social graph 200 may beconnected to each other by one or more edges 206. An edge 206 connectinga pair of nodes may represent a relationship between the pair of nodes.In particular embodiments, an edge 206 may include or represent one ormore data objects or attributes corresponding to the relationshipbetween a pair of nodes. As an example and not by way of limitation, afirst user may indicate that a second user is a “friend” of the firstuser. In response to this indication, social-networking system 160 maysend a “friend request” to the second user. If the second user confirmsthe “friend request,” social-networking system 160 may create an edge206 connecting the first user's user node 202 to the second user's usernode 202 in social graph 200 and store edge 206 as social-graphinformation in one or more of data stores 164. In the example of FIG. 2,social graph 200 includes an edge 206 indicating a friend relationbetween user nodes 202 of user “A” and user “B” and an edge indicating afriend relation between user nodes 202 of user “C” and user “B.”Although this disclosure describes or illustrates particular edges 206with particular attributes connecting particular user nodes 202, thisdisclosure contemplates any suitable edges 206 with any suitableattributes connecting user nodes 202. As an example and not by way oflimitation, an edge 206 may represent a friendship, family relationship,business or employment relationship, fan relationship (including, e.g.,liking, etc.), follower relationship, visitor relationship (including,e.g., accessing, viewing, checking-in, sharing, etc.), subscriberrelationship, superior/subordinate relationship, reciprocalrelationship, non-reciprocal relationship, another suitable type ofrelationship, or two or more such relationships. Moreover, although thisdisclosure generally describes nodes as being connected, this disclosurealso describes users or concepts as being connected. Herein, referencesto users or concepts being connected may, where appropriate, refer tothe nodes corresponding to those users or concepts being connected insocial graph 200 by one or more edges 206.

In particular embodiments, an edge 206 between a user node 202 and aconcept node 204 may represent a particular action or activity performedby a user associated with user node 202 toward a concept associated witha concept node 204. As an example and not by way of limitation, asillustrated in FIG. 2, a user may “like,” “attended,” “played,”“listened,” “cooked,” “worked at,” or “watched” a concept, each of whichmay correspond to an edge type or subtype. A concept-profile pagecorresponding to a concept node 204 may include, for example, aselectable “check in” icon (such as, for example, a clickable “check in”icon) or a selectable “add to favorites” icon. Similarly, after a userclicks these icons, social-networking system 160 may create a “favorite”edge or a “check in” edge in response to a user's action correspondingto a respective action. As another example and not by way of limitation,a user (user “C”) may listen to a particular song (“Imagine”) using aparticular application (a third-party online music application). In thiscase, social-networking system 160 may create a “listened” edge 206 anda “used” edge (as illustrated in FIG. 2) between user nodes 202corresponding to the user and concept nodes 204 corresponding to thesong and application to indicate that the user listened to the song andused the application. Moreover, social-networking system 160 may createa “played” edge 206 (as illustrated in FIG. 2) between concept nodes 204corresponding to the song and the application to indicate that theparticular song was played by the particular application. In this case,“played” edge 206 corresponds to an action performed by an externalapplication (the third-party online music application) on an externalaudio file (the song “Imagine”). Although this disclosure describesparticular edges 206 with particular attributes connecting user nodes202 and concept nodes 204, this disclosure contemplates any suitableedges 206 with any suitable attributes connecting user nodes 202 andconcept nodes 204. Moreover, although this disclosure describes edgesbetween a user node 202 and a concept node 204 representing a singlerelationship, this disclosure contemplates edges between a user node 202and a concept node 204 representing one or more relationships. As anexample and not by way of limitation, an edge 206 may represent boththat a user likes and has used at a particular concept. Alternatively,another edge 206 may represent each type of relationship (or multiplesof a single relationship) between a user node 202 and a concept node 204(as illustrated in FIG. 2 between user node 202 for user “E” and conceptnode 204 for “online music application”).

In particular embodiments, social-networking system 160 may create anedge 206 between a user node 202 and a concept node 204 in social graph200. As an example and not by way of limitation, a user viewing aconcept-profile page (such as, for example, by using a web browser or aspecial-purpose application hosted by the user's client system 130) mayindicate that he or she likes the concept represented by the conceptnode 204 by clicking or selecting a “Like” icon, which may cause theuser's client system 130 to send to social-networking system 160 amessage indicating the user's liking of the concept associated with theconcept-profile page. In response to the message, social-networkingsystem 160 may create an edge 206 between user node 202 associated withthe user and concept node 204, as illustrated by “like” edge 206 betweenthe user and concept node 204. In particular embodiments,social-networking system 160 may store an edge 206 in one or more datastores. In particular embodiments, an edge 206 may be automaticallyformed by social-networking system 160 in response to a particular useraction. As an example and not by way of limitation, if a first useruploads a picture, watches a movie, or listens to a song, an edge 206may be formed between user node 202 corresponding to the first user andconcept nodes 204 corresponding to those concepts. Although thisdisclosure describes forming particular edges 206 in particular manners,this disclosure contemplates forming any suitable edges 206 in anysuitable manner.

Training a Deep-Learning Model

Particular embodiments identify one or more entities as being relevantto a user using a deep-learning model. In particular embodiments, anentity may be any suitable entity that may be represented by a conceptnode 204 or user node 202 in social graph 200 (e.g., person, business,group, sports team, or celebrity). In particular embodiments, an entitymay be, as an example and not by way of limitation, a page hosted bysocial-networking system 160 (e.g., profile pages, place pages, businesspages), text content (e.g., one or more n-grams), visual content (e.g.,one or more images), audio content (e.g., one or more audio recordings),video content (e.g., one or more video clips), a label (e.g., a stickeror hashtag), any other suitable type of content, any other suitableentity, or any combination thereof. As used herein, labels may behashtags, emoji, stickers, ideograms, any other suitable textannotations, any other suitable characters, symbols, or images, whicheach may represent an idea or thing with or without using letters orwords, or any combination thereof. As used herein, hashtags may besingle tokens made up of natural language n-grams or abbreviations,prefixed with the character “#” (e.g., #blessed). As used herein,n-grams may be words or groups of words, any part of speech, punctuationmarks (e.g., “!”), colloquialisms (e.g., “go nuts”), acronyms (e.g.,“BRB”), abbreviations (e.g., “mgmt.”), exclamations (“ugh”),alphanumeric characters, symbols, written characters, accent marks, orany combination thereof.

FIG. 3 illustrates an example deep-learning model 310. Deep-learningmodel 310 may be a machine-learning model, a neural network, a latentneural network, any other suitable deep-learning model, or anycombination thereof. In particular embodiments, deep-learning model 310may have a plurality of layers of abstraction. Inputs 302, 304, 306, and308 may be any suitable number of entities. Outputs 312 may be one ormore embeddings of entities. The embedding space may be amulti-dimensional space (e.g., d-dimensional, where d is ahyper-parameter that controls capacity) and may include a plurality ofpoints corresponding to embeddings of entities. As used herein, anembedding of an entity refers to a representation of an entity in theembedding space. Although a particular number of input entities 302,304, 306, and 308 are illustrated in FIG. 3, deep-learning model 310 maygenerate embeddings of entities for any suitable number of inputentities 302, 304, 306, and 308.

In particular embodiments, deep-learning model 310 (e.g., a neuralnetwork) may include one or more indices that map entities to vectors in

^(d), where

denotes the set of real numbers and d is a hyper-parameter that controlscapacity. The vectors may be d-dimensional intensity vectors. As usedherein, intensity values may be any suitable values in the range of −1to 1. Each of the vector representations of entities may providecoordinates for respective points in the embedding space. Although aparticular number of input entities 302, 304, 306, and 308 areillustrated in FIG. 3, deep-learning model 310 may provide mappingsbetween any suitable number of entities 302, 304, 306, and 308 andvector representations.

Deep-learning model 310 may be trained to generate optimal embeddings ofentities. Deep-learning model 310 may include one or more indices (i.e.,dictionaries), which may be dynamically updated as the deep-learningmodel 310 is trained. The one or more indices may be generated during atraining phase of deep-learning model 310. In particular embodiments,the deep-learning model may include one or more indices trained to mapentities to vector representations. Deep-learning model 310 may be, forexample, a neural network or a latent neural network. Deep-learningmodel 310 may be initialized using a random distribution. That is,deep-learning model 310 may initially have randomly-assigned mappings(i.e., between entities 302, 304, 306, and 308 and vectorrepresentations, based on which embeddings of entities 302, 304, 306,and 308 may be generated). As an example and not by way of limitation,the random distribution may be a Gaussian distribution. The training mayresult in the one or more indices of deep-learning model 310 generatingmore optimal mappings than the initial mappings.

In particular embodiments, the deep-learning model 310 may have one ormore of the features of the deep-learning model described in co-pendingU.S. patent application Ser. No. 14/949,436, filed 23 Nov. 2015, and62/251,352, filed 5 Nov. 2015, which are incorporated by referenceherein.

Although this disclosure describes and illustrates particularembodiments of FIG. 3 as being implemented by social-networking system160, this disclosure contemplates any suitable embodiments of FIG. 3 asbeing implemented by any suitable platform or system. As an example, andnot by way of limitation, particular embodiments of FIG. 3 may beimplemented by client system 130, third-party system 170, or any othersuitable system. Furthermore, although this disclosure describes andillustrates particular components, devices, or systems carrying outparticular steps of the method of FIG. 3, this disclosure contemplatesany suitable combination of any suitable components, devices, or systemscarrying out any suitable steps of the method of FIG. 3.

FIG. 4 illustrates example dictionary 400 mapping entities 402, 404,406, and 408 to vector representations 412, 414, 416, and 418. Entities402, 404, 406, and 408 may be, as an example and not by way oflimitation, pages hosted by social-networking system 160. In particularembodiments, dictionary 400 may be generated by social-networking system160. In particular embodiments, dictionary 400 may map entities tovectors

^(d), where

denotes the set of real numbers and d is a hyper-parameter that controlscapacity. Vector representations 412, 414, 416, and 418 may bed-dimensional intensity vectors. As used herein, intensity values may beany suitable values in the range of −1 to 1. For illustrative purposesand not by way of limitation, the intensity values in example vectorrepresentations 412, 414, 416, and 418 are depicted as patterns in FIG.4. For example, dictionary 400 maps entity 402 to vector representation412, which is illustrated as the top row of cells of various patterns,adjacent to page 402, in dictionary 400. Although dictionary 400 isdepicted as being of a particular size (i.e., dimensions), this ismerely illustrative, not by way of limitation. Dictionary 400 may be ofany suitable sizes. Dictionary 400 may provide mappings between anysuitable number of entities and vector representations.

Each of the vector representations of the entities, determined usingdictionary 400 may provide coordinates for respective points in anembedding space. The embedding space may be a multi-dimensional space(e.g., d-dimensional) and may include a plurality of pointscorresponding to entity embeddings. As used herein, an entity embeddingrefers to a representation of an entity in the embedding space based onthe vector representation of the entity (e.g., determined usingdictionary 400). Each entity embedding may correspond to a respectivepoint in the embedding space. In particular embodiments, each entity maybe represented as a set of entities (e.g., a page may be represented asa set of text items appearing on the page), and the entity embedding maybe determined based on vector representations of the set of entities(e.g., a non-linear combination of the vector representations of textitems on the page). As another example and not by way of limitation, auser may be represented as a set of entities (e.g., as a set of pagesthat the user has “liked” in social-networking system 160), and anembedding of the user may be determined based on the vectorrepresentations of the entities in the set. Each of the entities in theset may, as described above, be represented, in turn, as a set ofentities (e.g., each entity vector representation may be based on vectorrepresentations of a set of entities). This technique for hierarchicallyrepresenting entities as sets of entities may continue as many levels assuitable. That is, as an example and not by way of limitation, eachentity may be represented as a set of entities, which may each berepresented as a set of entities, and so forth.

Dictionary 400 may be part of a deep-learning model. To generate optimalembeddings, the deep-learning model may be trained. Dictionary 400 maybe generated during a training phase of the deep-learning model. Thedeep-learning model may be, for example, a convolutional neural network.Dictionary 400 may be initialized using a random distribution. That is,dictionary 400 may initially have randomly-assigned mappings. As anexample and not by way of limitation, the random distribution may be aGaussian distribution. The training may result in dictionary 400generating more optimal mappings than the initial mappings.

Although this disclosure describes and illustrates particularembodiments of FIG. 4 as being implemented by social-networking system160, this disclosure contemplates any suitable embodiments of FIG. 4 asbeing implemented by any suitable platform or system. As an example, andnot by way of limitation, particular embodiments of FIG. 4 may beimplemented by client system 130 or third-party system 170. Furthermore,although this disclosure describes and illustrates particularcomponents, devices, or systems carrying out particular steps of themethod of FIG. 4, this disclosure contemplates any suitable combinationof any suitable components, devices, or systems carrying out anysuitable steps of the method of FIG. 4.

FIG. 5 illustrates an example method for determining an embedding ofuser using a deep-learning model. In particular embodiments, a user maybe represented as a set of entities with which the user has interactedin social-networking system 160. In other words, an embedding may bedetermined for the user based on the vector representations of a set ofentities. The set of entities may be an unordered set. At step 510,social-networking system 160 may access a first set of entities that theuser has interacted with in social-networking system 160. The user mayinteract with the entities of the first set of entities insocial-networking system 160 using any suitable social-networkingaction. As an example and not by way of limitation, the interaction maybe an expression of affinity for the entity. As another example and notby way of limitation, the user may interact with an entity by “liking”the entity, sharing the entity, publishing a post that includes areference to the entity, composing and sending a message to another userin which the entity is mentioned, performing any other suitablesocial-networking action, or any combination thereof. In particularembodiments, a first node in social graph 200, described in connectionwith FIG. 2, may correspond to the user, and a plurality of second nodesin social graph 200 may each correspond to an entity. The user mayinteract with the first set of entities by a social-networking action ofthe user, and the social-networking action may be taken with respect tothe first node and a respective second node corresponding to arespective entity of the first set of entities. For example, thesocial-networking action may result in the creation of an edge in socialgraph 200 between a first node corresponding to the user and a secondnode corresponding to an entity. The first set of entities may includeany suitable number of entities. In the illustrated example of FIG. 5,the first set of entities is shown as including entities 502, 504, 506,and 508: pages corresponding to “ice hockey,” “Brooklyn,” “coffee,” and“apple pie.” Entities 502, 504, 506, and 508 of the first set ofentities may be, as an example and not by way of limitation, pageshosted by social-networking system 160 that the user has “liked” orotherwise expressed affinity for in social-networking system 160.

At step 520, social-networking system 160 may determine a first set ofvector representations for the first set of entities. In particularembodiments, social-networking system 160 may map each entity 502, 504,506, and 508 to a respective vector representation (e.g., one-to-onemapping), using, as an example and not by way of limitation, dictionary400, which is generated using the deep-learning model. Each entity 502,504, 506, and 508 may be mapped to a respective vector representation522, 524, 526, and 528 using any of the techniques described above inconnection with FIG. 4 or any other suitable techniques.

At step 530, social-networking system 160 may select a target entityfrom the first set of entities. In the illustrated example of FIG. 5,social-networking system 160 selects entity 508 as the target entity. Inparticular embodiments, the target entity may be selected randomly. Inparticular embodiments, the target entity may be selected by thedeep-learning model. In particular embodiments, the target entity may beselected using a heuristic method for finding hard examples of entities.For example, entities may be clustered or hashed in the embedding space(e.g., using the deep-learning model), and hard examples may be entitiesin the same cluster or hash in the embedding space. In the illustratedexample of FIG. 5, the target entity is depicted as entity 508, whichcorresponds to a page for “apple pie” that is hosted bysocial-networking system 160.

At step 540, social-networking system 160 may remove the vectorrepresentation of the target entity selected at step 530 from the firstset of vector representations. In the illustrated example of FIG. 5, thevector representation 528 of the target entity 508 has been removed fromthe first set of vector representations. The resultant first set ofvector representations is shown as including vector representations 522,524, and 526, which correspond respectively to entities 502, 504, and506.

At step 550, social-networking system 160 may combine the remainingvector representations 522, 524, and 526. Any suitable technique may beused to combine vector representations 522, 524, and 526 into a singlevector representation 560, including, as an example and not by way oflimitation, convolution, averaging, any other suitable non-linearcombination technique, any other suitable technique, or any combinationthereof. In the illustrated example of FIG. 5, social-networking system160 combines the three vectors 522, 524, and 526 (e.g., a 3-tapconvolution) and then performs a max pooling operation to yield onevector representation 560 of the user.

An embedding of the user may be determined based on the combination ofthe vector representations. In particular embodiments, social-networkingsystem 160 may determine an embedding of the user based on the vectorrepresentation 560. As used herein, an embedding of a user may refer toa representation of the user in an embedding space based on the combinedvector representation 560 of the user. Coordinates for a point in anembedding space may be determined based on vector representation 560 ofthe user. In particular embodiments, the deep-learning model may be usedto generate a plurality of user embeddings. Each of these userembeddings may be based on a respective vector representation thatcorresponds to a particular point in an embedding space. In particularembodiments, the deep-learning model may be used to generate a pluralityof embeddings of entities. Each of the entity embeddings may be based ona respective vector representation that corresponds to a particularpoint in the embedding space. In particular embodiments, the point inthe embedding space corresponding to the user may be proximate to pointsin the embedding space corresponding to entities in the first set ofentities (i.e., entities with which the user has interacted insocial-networking system 160).

Although this disclosure describes and illustrates particularembodiments of FIG. 5 as being implemented by social-networking system160, this disclosure contemplates any suitable embodiments of FIG. 5occurring on any suitable interface and as being implemented by anysuitable platform or system. As an example, and not by way oflimitation, particular embodiments of FIG. 5 may be implemented byclient system 130 or third-party system 170. Furthermore, although thisdisclosure describes and illustrates particular components, devices, orsystems carrying out particular steps of the method of FIG. 5, thisdisclosure contemplates any suitable combination of any suitablecomponents, devices, or systems carrying out any suitable steps of themethod of FIG. 5.

FIG. 6 illustrates an example method for training a deep-learning model.The deep-learning model may be trained to minimize or reduce errorbetween the vector representation of the user and the vectorrepresentations of entities that are relevant to the user. In particularembodiments, a goal of the training of the deep-learning model may be togenerate entity embeddings for relevant entities that correspond topoints in an embedding space that are proximate to the pointcorresponding to the user embedding. Relevance, between a user and anentity, may be determined based on any suitable factors, including forexample and not by way of limitation, the user's prior interaction withentities in social-networking system 160, any other suitablesocial-networking information of the user, or both (e.g., informationregarding social-networking connections of the user). In particularembodiments, the deep-learning model may be trained using the targetentity 508 (e.g., page corresponding to “apple pie”) selected at step530 of FIG. 5 as a supervisory signal. As an example and not by way oflimitation, the deep-learning model may trained so that the embedding oftarget entity 508 is more proximate to the user embedding in theembedding space than the embeddings of entities with which the user hasnot interacted.

At step 610, social-networking system 160 may determine vectorrepresentations for a second set of entities using the deep-learningmodel. As an example and not by way of limitation, the vectorrepresentations of the entities in the second set of entities may bedetermined using a dictionary 400. In particular embodiments, the secondset of entities may be randomly selected from a plurality of entities.In particular embodiments, the second set of entities may include one ormore entities with which the user has not interacted insocial-networking system 160. The second set of entities may beconsidered, for purposes of training the deep-learning model, negativeexamples (i.e., entities not known to be relevant to the user), whereasthe first set of entities may be positive examples (i.e., entities knownto be relevant to the user). The second set of entities may include anysuitable number of entities. In an example and not by way of limitation,the second set of entities may include fifty entities that are randomlyselected by social-networking system 160. In particular embodiments, thenumber of entities in the second set of entities may be based on a lossfunction of the deep-learning model. As an example and not by way oflimitation, the number of entities in the second set of entities may bea fixed hyper-parameter based on the available computational budget. Inparticular embodiments, a loss function may be used to train thedeep-learning model. To learn the optimal weights (i.e., parameters) ofthe deep-learning model, social-networking system 160 may minimize aloss function (i.e., minimize error). In particular embodiments,social-networking system 160 may update vector representations of thesecond set of entities in a batch. As an example and not by way oflimitation, a set of negative examples of entities (e.g., entities notknown to be relevant to the user) may be selected as a batch of anarbitrary, fixed size. As an example and not by way of limitation, thesize of the batch (i.e., the number of entities in the second set ofentities) may be a parameter that is fixed in advance across alltraining of the deep-learning model. As another example and not by wayof limitation, the size of the batch may also change over time duringtraining or the deep-learning model. As another example and not by wayof limitation, the size of the batch may be a function of thedeep-learning model. Social-networking system 160 may then perform abatch gradient update over this second set of entities (i.e., the batchof negative examples).

At step 620, social-networking system 160 may determine similarityscores by comparing the vector representation of the user 560 with thevector representations determined at step 610 of each entity of thesecond set of entities. Thus, social-networking system 160 may determinea measure of similarity to the user for each entity of the second set ofentities. Although FIG. 6 is depicted as comparing vector representationof the user 560 to a single vector representation of an entity 612 ofthe second set of entities, it will be understood that this is forillustrative purposes only and not by way of limitation. Rather, vectorrepresentation of the user 560 may be compared to a vectorrepresentation of each entity of the second set of entities so that asimilarity score may be computed for each entity of the second set ofentities. In particular embodiments, a similarity score between the userand an entity of the second set of entities may be determined based onan angle between the vector representations of the user 560 and thevector representation of the entity. In particular embodiments, asimilarity score between the user and an entity of the second set ofentities may be determined by computing a dot product of vectorrepresentation of the user 560 and the vector representation of theentity. In particular embodiments, a similarity score may be determinedbased on Euclidean distance, cosine similarity, or any other suitabletechnique for computing a measure of pairwise relevance of points in anembedding space.

At step 630, social-networking system 160 may determine a similarityscore by comparing the vector representation of the user 560 with thevector representation 528 of the target entity 508, determined at step520 of FIG. 5. Thus, social-networking system 160 may determine ameasure of similarity to the user for the target entity. The similarityscore between the user and the target entity 508 may be determined usingany of the techniques described in connection with step 620 or using anyother suitable technique.

In particular embodiments, social-networking system 160 may rank thetarget entity and the entities of the second set of entities againsteach other based on the similarity scores determined for each at steps620 and 630. Ranking may be accomplished by assigned each entity anumerical rank, assigning each entity a position in a single-columnedtable, or using any other suitable technique for ranking the entitiesagainst each other based on the similarity scores of each. As an exampleand not by way of limitation, social-networking system 160 may assigneach entity a numerical rank, and a higher numerical rank may correspondto a higher similarity score.

At step 640, social-networking system 160 may update vectorrepresentations of one or more entities in the second set of entitiesbased on the similarity scores determined at steps 620 and 630. Inparticular embodiments, the vector representations may be updated basedon a comparison of the similarity score determined at step 620 with eachof the similarity scores determined at step 630. In particularembodiments, social-networking system 160 may assign rankings to each ofthe target entity and the second set of entities, and the one or moreweights of the deep-learning model may be updated further based on therankings. As described above, the deep-learning model may trained sothat the embedding of target entity 508 is more proximate to the userembedding in the embedding space than the embeddings of entities withwhich the user has not interacted. Thus, the deep-learning model, may betrained so that each of the entities of the second set of entities(i.e., negative samples) has a lower similarity score than the targetentity (i.e., a positive sample). In other words, the deep-learningmodel may be trained so that each of the entities of the second set ofentities is ranked lower than the target entity. Social-networkingsystem 160 may determine that one or more of the entities of the secondset of entities are ranked above or have higher similarity scores thanthe target entity (i.e., have corresponding embeddings that are moreproximate to the user embedding in the embedding space), andsocial-networking system 160 may update vector representations of one ormore entities of the second set of entities. As an example and not byway of limitation, an entity in the second set of entities may be a pagehosted by social-networking system 160 that corresponds to “running.” Inthe same example, social-networking system 160 may determine that the“running” entity has a higher similarity score (i.e., computed betweenthe vector representation of the user 560 and the vector representationof the “running” entity) than the target entity (i.e., computed betweenthe vector representation of the user 560 and the vector representation528 of the target entity 508). Social-networking system 160 may updatethe vector representation of the “running” entity so that an updatedsimilarity score for the “running” entity (i.e., computed between thevector representation of the user 560 and the updated vectorrepresentation of the “running” entity) is less than the similarityscore for the target entity 508.

In particular embodiments, social-networking system 160 may update thevector representations of the one or more entities of the second set ofentities by updating one or more weights of the deep-learning model.Initial values of the one or more weights of the deep-learning model maybe randomly determined (e.g., using a Gaussian distribution). Inparticular embodiments, one or more of the weights of the deep-learningmodel may be updated to minimize error using similarity scoresdetermined at steps 620, between the user and the entities of the secondset of entities, and at step 630, between the user and the target entity508. In particular embodiments, one or more weights of the deep-learningmodel may be updated to minimize error given by Eq. 1. The weights ofthe deep-learning model may be updated to yield better vectorrepresentations for the second set of entities. This method described insteps 610-640 may be repeated, and a stochastic gradient descentfunction may be used to gradually refine the weights. Training thedeep-learning model by updating the weights may improve the mappings ofdictionary 400.

Although this disclosure describes and illustrates particularembodiments of FIG. 6 as being implemented by social-networking system160, this disclosure contemplates any suitable embodiments of FIG. 6occurring on any suitable interface and as being implemented by anysuitable platform or system. As an example, and not by way oflimitation, particular embodiments of FIG. 6 may be implemented byclient system 130 or third-party system 170. Furthermore, although thisdisclosure describes and illustrates particular components, devices, orsystems carrying out particular steps of the method of FIG. 6, thisdisclosure contemplates any suitable combination of any suitablecomponents, devices, or systems carrying out any suitable steps of themethod of FIG. 6.

FIG. 7 illustrates an example view of an embedding space 700. Embeddingspace 700 includes a point 710 corresponding to an embedding of a user710 and a plurality of points 730 corresponding to a plurality of entityembeddings. The plurality of entity embeddings may be determined basedon the vector representations of the entities determined using thedeep-learning model (e.g., after it has been trained as described inconnection with FIG. 5-6), and the entity embeddings may be representedin embedding space 700 as points 730. The user embedding may bedetermined based on the vector representation of the user 560 determinedusing the deep-learning model, as described in connection with FIG. 5,and the user embedding may be represented in embedding space 700 aspoint 710. Although embedding space 700 is illustrated as being athree-dimensional space, it will be understood that this is forillustrative purposes only, not by way of limitation, and embeddingspace 700 may be of any suitable dimension. As an example and not by wayof limitation, embedding space 700 may be d-dimensional, and the vectorrepresentations on which the embeddings are based (e.g., user and entityembeddings) may be d-dimensional intensity vectors, where d denotes anysuitable number of dimensions. Although embedding space 700 is depictedas having only one embedding of a user 710, it will be understood thatthis is for illustrative purposes only, not by way of limitation, andembedding space 700 may include a plurality of points corresponding to aplurality of other users of social-networking system 160. As an exampleand not by way of limitation, the steps of FIGS. 5 and 6 may be repeatedfor other users, to generate embeddings for the other users. In the sameexample, the deep-learning model may be trained so that the entityembeddings are additionally based on target entities from sets ofentities representing the other users.

In particular embodiments, embedding space 700 may be used to predictentities (e.g., entities in the second set of entities) that arerelevant to the user. In particular embodiments, social-networkingsystem 160 may identify one or more points corresponding to embeddingsof entities that are relevant to the user using a search algorithm. Inparticular embodiments, an entity may be relevant to the user if thereis a predicted likelihood that the user will interact with the entity.The search algorithm may be applied to embedding space 700 to identifypoints corresponding to entity embeddings that are within a thresholddistance of point 710 corresponding to the embedding of the user. Eachof these entity embeddings may be associated with a respective entitythat may be identified as relevant to the user. In the illustratedexample of FIG. 7, the threshold distance is depicted as an area 720 inembedding space 700. As an example and not by way of limitation, point710 may be a point corresponding to the embedding of the user, and thepoints identified as being within area 720 of point 710 may includepoints corresponding to embeddings of entities including pages hosted bysocial-networking system 160 that correspond to “Wayne Gretzky” and“Boston Crème Pie.” In particular embodiments, social-networking system160 may use any suitable technique for identifying one or more entitiesthat are relevant to a user. As an example and not by way of limitation,social-networking system 160 may use locality-sensitive hashing,hierarchical clustering techniques, ball tree techniques, binary searchtree techniques, a space-partitioning data structure for organizingpoints in a k-dimensional space (e.g., a k-dimensional tree),quantization, any other suitable search algorithm or technique, or anycombination thereof. As another example and not by way of limitation,social-networking system 160 may identify a predetermined number ofentities as relevant to a user based on the rankings assigned to theentities based on the computed similarity scores, as described inconnection with FIG. 6. As an example and not by way of limitation,after the deep-learning model has completed training, social-networkingsystem 160 may select the top ten entities based on their rankings(excluding the target entity 508, which the user is known to haveinteracted with) as relevant to the user.

In particular embodiments, social-networking system 160 may send the oneor more entities identified as being relevant to the user to a user'sclient system 130 for display to the user. In particular embodiments,the one or more identified entities may be displayed to the user at aninterface of an application running on the user's client system (e.g.,an application associated with social-networking system 160 or a webbrowser). As an example and not by way of limitation, the user may entera search query via an interface of an application running on the user'sclient system 130 (e.g., an application associated withsocial-networking system 160), and social-networking system 160 mayprovide search results based on the rankings of the entities that areresponsive to the search query (e.g., identified entities may appearhigher in search results) as suggestions to the user in the application.As another example and not by way of limitation, the one or moreidentified entities may be delivered to the user as a notification(e.g., You like “ice hockey,” so you may also like “Wayne Gretzky”).

In particular embodiments, an entity identified as relevant to the usermay be on with which the user will interact, as predicted bysocial-networking system 160. In particular embodiments,social-networking system 160 may use embedding space 700 to predict theentities with which the user will positively interact. As an example andnot by way of limitation, social-networking system 160 may apply asearch algorithm to identify one or more entity embeddings (with whichthe user has not yet interacted) within a predetermined distance 720 ofthe embedding of the user (e.g., point 710). The social-networkingsystem 160 may then identify entities (e.g., pages hosted bysocial-networking system 160) associated with these identified entityembeddings to predict an entity with which the user may positivelyinteract (e.g., “like,” click on, or otherwise interact with onsocial-networking system 160).

Although this disclosure describes and illustrates particularembodiments of FIG. 7 as being implemented by social-networking system160, this disclosure contemplates any suitable embodiments of FIG. 7occurring on any suitable interface and as being implemented by anysuitable platform or system. As an example, and not by way oflimitation, particular embodiments of FIG. 7 may be implemented byclient system 130 or third-party system 170. Furthermore, although thisdisclosure describes and illustrates particular components, devices, orsystems carrying out particular steps of the method of FIG. 7, thisdisclosure contemplates any suitable combination of any suitablecomponents, devices, or systems carrying out any suitable steps of themethod of FIG. 7.

FIG. 8 illustrates an example method 800 for updating vectorrepresentations of entities using a deep-learning model. The method maybegin at step 810, where social-networking system 160 may access a firstset of entities that a user of a social-networking system has interactedwith in the social-networking system and a second set of entities in thesocial-networking system. At step 820, social-networking system 160 maydetermine a first set of vector representations of the first set ofentities using a deep-learning model. At step 830, social-networkingsystem 160 may select a target entity from the first set of entities. Atstep 840, social-networking system 160 may remove from the first set ofvector representations the vector representation of the target entity.At step 850, social-networking system 160 may combine the remainingvector representations in the first set of vector representations todetermine a vector representation of the user. At step 860,social-networking system 160 may determine a second set of vectorrepresentations of the second set of entities using the deep-learningmodel. At step 870, social-networking system 160 may compute asimilarity score between the target entity and the user by comparing thevector representation of the user with the vector representation of thetarget entity. At step 880, social-networking system 160 may computesimilarity scores between the user and the entities in the second set ofentities by comparing the vector representation of the user with thevector representations of the entities in the second set of entities. Atstep 890, social-networking system 160 may update the vectorrepresentations of one or more entities in the second set of entitiesbased on the similarity scores using the deep-learning model.

Particular embodiments may repeat one or more steps of the method ofFIG. 8, where appropriate. Although this disclosure describes andillustrates particular steps of the method of FIG. 8 as occurring in aparticular order, this disclosure contemplates any suitable steps of themethod of FIG. 8 occurring in any suitable order. Moreover, althoughthis disclosure describes and illustrates an example method for updatingvector representations of entities using a deep-learning model includingthe particular steps of the method of FIG. 8, this disclosurecontemplates any suitable method for updating vector representations ofentities using a deep-learning model including any suitable steps, whichmay include all, some, or none of the steps of the method of FIG. 8,where appropriate. Furthermore, although this disclosure describes andillustrates particular components, devices, or systems carrying outparticular steps of the method of FIG. 8, this disclosure contemplatesany suitable combination of any suitable components, devices, or systemscarrying out any suitable steps of the method of FIG. 8.

Social Graph Affinity and Coefficient

In particular embodiments, social-networking system 160 may determinethe social-graph affinity (which may be referred to herein as“affinity”) of various social-graph entities for each other. Affinitymay represent the strength of a relationship or level of interestbetween particular objects associated with the online social network,such as users, concepts, content, actions, advertisements, other objectsassociated with the online social network, or any suitable combinationthereof. Affinity may also be determined with respect to objectsassociated with third-party systems 170 or other suitable systems. Anoverall affinity for a social-graph entity for each user, subjectmatter, or type of content may be established. The overall affinity maychange based on continued monitoring of the actions or relationshipsassociated with the social-graph entity. Although this disclosuredescribes determining particular affinities in a particular manner, thisdisclosure contemplates determining any suitable affinities in anysuitable manner.

In particular embodiments, social-networking system 160 may measure orquantify social-graph affinity using an affinity coefficient (which maybe referred to herein as “coefficient”). The coefficient may representor quantify the strength of a relationship between particular objectsassociated with the online social network. The coefficient may alsorepresent a probability or function that measures a predictedprobability that a user will perform a particular action based on theuser's interest in the action. In this way, a user's future actions maybe predicted based on the user's prior actions, where the coefficientmay be calculated at least in part on the history of the user's actions.Coefficients may be used to predict any number of actions, which may bewithin or outside of the online social network. As an example and not byway of limitation, these actions may include various types ofcommunications, such as sending messages, posting content, or commentingon content; various types of observation actions, such as accessing orviewing profile pages, media, or other suitable content; various typesof coincidence information about two or more social-graph entities, suchas being in the same group, tagged in the same photograph, checked-in atthe same location, or attending the same event; or other suitableactions. Although this disclosure describes measuring affinity in aparticular manner, this disclosure contemplates measuring affinity inany suitable manner.

In particular embodiments, social-networking system 160 may use avariety of factors to calculate a coefficient. These factors mayinclude, for example, user actions, types of relationships betweenobjects, location information, other suitable factors, or anycombination thereof. In particular embodiments, different factors may beweighted differently when calculating the coefficient. The weights foreach factor may be static or the weights may change according to, forexample, the user, the type of relationship, the type of action, theuser's location, and so forth. Ratings for the factors may be combinedaccording to their weights to determine an overall coefficient for theuser. As an example and not by way of limitation, particular useractions may be assigned both a rating and a weight while a relationshipassociated with the particular user action is assigned a rating and acorrelating weight (e.g., so the weights total 100%). To calculate thecoefficient of a user towards a particular object, the rating assignedto the user's actions may comprise, for example, 60% of the overallcoefficient, while the relationship between the user and the object maycomprise 40% of the overall coefficient. In particular embodiments, thesocial-networking system 160 may consider a variety of variables whendetermining weights for various factors used to calculate a coefficient,such as, for example, the time since information was accessed, decayfactors, frequency of access, relationship to information orrelationship to the object about which information was accessed,relationship to social-graph entities connected to the object, short- orlong-term averages of user actions, user feedback, other suitablevariables, or any combination thereof. As an example and not by way oflimitation, a coefficient may include a decay factor that causes thestrength of the signal provided by particular actions to decay withtime, such that more recent actions are more relevant when calculatingthe coefficient. The ratings and weights may be continuously updatedbased on continued tracking of the actions upon which the coefficient isbased. Any type of process or algorithm may be employed for assigning,combining, averaging, and so forth the ratings for each factor and theweights assigned to the factors. In particular embodiments,social-networking system 160 may determine coefficients usingmachine-learning algorithms trained on historical actions and past userresponses, or data farmed from users by exposing them to various optionsand measuring responses. Although this disclosure describes calculatingcoefficients in a particular manner, this disclosure contemplatescalculating coefficients in any suitable manner.

In particular embodiments, social-networking system 160 may calculate acoefficient based on a user's actions. Social-networking system 160 maymonitor such actions on the online social network, on a third-partysystem 170, on other suitable systems, or any combination thereof. Anysuitable type of user actions may be tracked or monitored. Typical useractions include viewing profile pages, creating or posting content,interacting with content, tagging or being tagged in images, joininggroups, listing and confirming attendance at events, checking-in atlocations, liking particular pages, creating pages, and performing othertasks that facilitate social action. In particular embodiments,social-networking system 160 may calculate a coefficient based on theuser's actions with particular types of content. The content may beassociated with the online social network, a third-party system 170, oranother suitable system. The content may include users, profile pages,posts, news stories, headlines, instant messages, chat roomconversations, emails, advertisements, pictures, video, music, othersuitable objects, or any combination thereof. Social-networking system160 may analyze a user's actions to determine whether one or more of theactions indicate an affinity for subject matter, content, other users,and so forth. As an example and not by way of limitation, if a userfrequently posts content related to “coffee” or variants thereof,social-networking system 160 may determine the user has a highcoefficient with respect to the concept “coffee”. Particular actions ortypes of actions may be assigned a higher weight and/or rating thanother actions, which may affect the overall calculated coefficient. Asan example and not by way of limitation, if a first user emails a seconduser, the weight or the rating for the action may be higher than if thefirst user simply views the user-profile page for the second user.

In particular embodiments, social-networking system 160 may calculate acoefficient based on the type of relationship between particularobjects. Referencing the social graph 200, social-networking system 160may analyze the number and/or type of edges 206 connecting particularuser nodes 202 and concept nodes 204 when calculating a coefficient. Asan example and not by way of limitation, user nodes 202 that areconnected by a spouse-type edge (representing that the two users aremarried) may be assigned a higher coefficient than a user nodes 202 thatare connected by a friend-type edge. In other words, depending upon theweights assigned to the actions and relationships for the particularuser, the overall affinity may be determined to be higher for contentabout the user's spouse than for content about the user's friend. Inparticular embodiments, the relationships a user has with another objectmay affect the weights and/or the ratings of the user's actions withrespect to calculating the coefficient for that object. As an exampleand not by way of limitation, if a user is tagged in a first photo, butmerely likes a second photo, social-networking system 160 may determinethat the user has a higher coefficient with respect to the first photothan the second photo because having a tagged-in-type relationship withcontent may be assigned a higher weight and/or rating than having alike-type relationship with content. In particular embodiments,social-networking system 160 may calculate a coefficient for a firstuser based on the relationship one or more second users have with aparticular object. In other words, the connections and coefficientsother users have with an object may affect the first user's coefficientfor the object. As an example and not by way of limitation, if a firstuser is connected to or has a high coefficient for one or more secondusers, and those second users are connected to or have a highcoefficient for a particular object, social-networking system 160 maydetermine that the first user should also have a relatively highcoefficient for the particular object. In particular embodiments, thecoefficient may be based on the degree of separation between particularobjects. The lower coefficient may represent the decreasing likelihoodthat the first user will share an interest in content objects of theuser that is indirectly connected to the first user in the social graph200. As an example and not by way of limitation, social-graph entitiesthat are closer in the social graph 200 (i.e., fewer degrees ofseparation) may have a higher coefficient than entities that are furtherapart in the social graph 200.

In particular embodiments, social-networking system 160 may calculate acoefficient based on location information. Objects that aregeographically closer to each other may be considered to be more relatedor of more interest to each other than more distant objects. Inparticular embodiments, the coefficient of a user towards a particularobject may be based on the proximity of the object's location to acurrent location associated with the user (or the location of a clientsystem 130 of the user). A first user may be more interested in otherusers or concepts that are closer to the first user. As an example andnot by way of limitation, if a user is one mile from an airport and twomiles from a gas station, social-networking system 160 may determinethat the user has a higher coefficient for the airport than the gasstation based on the proximity of the airport to the user.

In particular embodiments, social-networking system 160 may performparticular actions with respect to a user based on coefficientinformation. Coefficients may be used to predict whether a user willperform a particular action based on the user's interest in the action.A coefficient may be used when generating or presenting any type ofobjects to a user, such as advertisements, search results, news stories,media, messages, notifications, or other suitable objects. Thecoefficient may also be utilized to rank and order such objects, asappropriate. In this way, social-networking system 160 may provideinformation that is relevant to user's interests and currentcircumstances, increasing the likelihood that they will find suchinformation of interest. In particular embodiments, social-networkingsystem 160 may generate content based on coefficient information.Content objects may be provided or selected based on coefficientsspecific to a user. As an example and not by way of limitation, thecoefficient may be used to generate media for the user, where the usermay be presented with media for which the user has a high overallcoefficient with respect to the media object. As another example and notby way of limitation, the coefficient may be used to generateadvertisements for the user, where the user may be presented withadvertisements for which the user has a high overall coefficient withrespect to the advertised object. In particular embodiments,social-networking system 160 may generate search results based oncoefficient information. Search results for a particular user may bescored or ranked based on the coefficient associated with the searchresults with respect to the querying user. As an example and not by wayof limitation, search results corresponding to objects with highercoefficients may be ranked higher on a search-results page than resultscorresponding to objects having lower coefficients.

In particular embodiments, social-networking system 160 may calculate acoefficient in response to a request for a coefficient from a particularsystem or process. To predict the likely actions a user may take (or maybe the subject of) in a given situation, any process may request acalculated coefficient for a user. The request may also include a set ofweights to use for various factors used to calculate the coefficient.This request may come from a process running on the online socialnetwork, from a third-party system 170 (e.g., via an API or othercommunication channel), or from another suitable system. In response tothe request, social-networking system 160 may calculate the coefficient(or access the coefficient information if it has previously beencalculated and stored). In particular embodiments, social-networkingsystem 160 may measure an affinity with respect to a particular process.Different processes (both internal and external to the online socialnetwork) may request a coefficient for a particular object or set ofobjects. Social-networking system 160 may provide a measure of affinitythat is relevant to the particular process that requested the measure ofaffinity. In this way, each process receives a measure of affinity thatis tailored for the different context in which the process will use themeasure of affinity.

In connection with social-graph affinity and affinity coefficients,particular embodiments may utilize one or more systems, components,elements, functions, methods, operations, or steps disclosed in U.S.patent application Ser. No. 11/503,093, filed 11 Aug. 2006, U.S. patentapplication Ser. No. 12/977,027, filed 22 Dec. 2010, U.S. patentapplication Ser. No. 12/978,265, filed 23 Dec. 2010, and U.S. patentapplication Ser. No. 13/632,869, filed 1 Oct. 2012, each of which isincorporated by reference herein.

Systems and Methods

FIG. 9 illustrates an example computer system 900. In particularembodiments, one or more computer systems 900 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 900 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 900 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 900.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems900. This disclosure contemplates computer system 900 taking anysuitable physical form. As example and not by way of limitation,computer system 900 may be an embedded computer system, a system-on-chip(SOC), a single-board computer system (SBC) (such as, for example, acomputer-on-module (COM) or system-on-module (SOM)), a desktop computersystem, a laptop or notebook computer system, an interactive kiosk, amainframe, a mesh of computer systems, a mobile telephone, a personaldigital assistant (PDA), a server, a tablet computer system, or acombination of two or more of these. Where appropriate, computer system900 may include one or more computer systems 900; be unitary ordistributed; span multiple locations; span multiple machines; spanmultiple data centers; or reside in a cloud, which may include one ormore cloud components in one or more networks. Where appropriate, one ormore computer systems 900 may perform without substantial spatial ortemporal limitation one or more steps of one or more methods describedor illustrated herein. As an example and not by way of limitation, oneor more computer systems 900 may perform in real time or in batch modeone or more steps of one or more methods described or illustratedherein. One or more computer systems 900 may perform at different timesor at different locations one or more steps of one or more methodsdescribed or illustrated herein, where appropriate.

In particular embodiments, computer system 900 includes a processor 902,memory 904, storage 906, an input/output (I/O) interface 908, acommunication interface 910, and a bus 912. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 902 includes hardware for executinginstructions, such as those making up a computer program. As an exampleand not by way of limitation, to execute instructions, processor 902 mayretrieve (or fetch) the instructions from an internal register, aninternal cache, memory 904, or storage 906; decode and execute them; andthen write one or more results to an internal register, an internalcache, memory 904, or storage 906. In particular embodiments, processor902 may include one or more internal caches for data, instructions, oraddresses. This disclosure contemplates processor 902 including anysuitable number of any suitable internal caches, where appropriate. Asan example and not by way of limitation, processor 902 may include oneor more instruction caches, one or more data caches, and one or moretranslation lookaside buffers (TLBs). Instructions in the instructioncaches may be copies of instructions in memory 904 or storage 906, andthe instruction caches may speed up retrieval of those instructions byprocessor 902. Data in the data caches may be copies of data in memory904 or storage 906 for instructions executing at processor 902 tooperate on; the results of previous instructions executed at processor902 for access by subsequent instructions executing at processor 902 orfor writing to memory 904 or storage 906; or other suitable data. Thedata caches may speed up read or write operations by processor 902. TheTLBs may speed up virtual-address translation for processor 902. Inparticular embodiments, processor 902 may include one or more internalregisters for data, instructions, or addresses. This disclosurecontemplates processor 902 including any suitable number of any suitableinternal registers, where appropriate. Where appropriate, processor 902may include one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 902. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 904 includes main memory for storinginstructions for processor 902 to execute or data for processor 902 tooperate on. As an example and not by way of limitation, computer system900 may load instructions from storage 906 or another source (such as,for example, another computer system 900) to memory 904. Processor 902may then load the instructions from memory 904 to an internal registeror internal cache. To execute the instructions, processor 902 mayretrieve the instructions from the internal register or internal cacheand decode them. During or after execution of the instructions,processor 902 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor902 may then write one or more of those results to memory 904. Inparticular embodiments, processor 902 executes only instructions in oneor more internal registers or internal caches or in memory 904 (asopposed to storage 906 or elsewhere) and operates only on data in one ormore internal registers or internal caches or in memory 904 (as opposedto storage 906 or elsewhere). One or more memory buses (which may eachinclude an address bus and a data bus) may couple processor 902 tomemory 904. Bus 912 may include one or more memory buses, as describedbelow. In particular embodiments, one or more memory management units(MMUs) reside between processor 902 and memory 904 and facilitateaccesses to memory 904 requested by processor 902. In particularembodiments, memory 904 includes random access memory (RAM). This RAMmay be volatile memory, where appropriate Where appropriate, this RAMmay be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 904 may include one ormore memories 904, where appropriate. Although this disclosure describesand illustrates particular memory, this disclosure contemplates anysuitable memory.

In particular embodiments, storage 906 includes mass storage for data orinstructions. As an example and not by way of limitation, storage 906may include a hard disk drive (HDD), a floppy disk drive, flash memory,an optical disc, a magneto-optical disc, magnetic tape, or a UniversalSerial Bus (USB) drive or a combination of two or more of these. Storage906 may include removable or non-removable (or fixed) media, whereappropriate. Storage 906 may be internal or external to computer system900, where appropriate. In particular embodiments, storage 906 isnon-volatile, solid-state memory. In particular embodiments, storage 906includes read-only memory (ROM). Where appropriate, this ROM may bemask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM),electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM),or flash memory or a combination of two or more of these. Thisdisclosure contemplates mass storage 906 taking any suitable physicalform. Storage 906 may include one or more storage control unitsfacilitating communication between processor 902 and storage 906, whereappropriate. Where appropriate, storage 906 may include one or morestorages 906. Although this disclosure describes and illustratesparticular storage, this disclosure contemplates any suitable storage.

In particular embodiments, I/O interface 908 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 900 and one or more I/O devices. Computer system900 may include one or more of these I/O devices, where appropriate. Oneor more of these I/O devices may enable communication between a personand computer system 900. As an example and not by way of limitation, anI/O device may include a keyboard, keypad, microphone, monitor, mouse,printer, scanner, speaker, still camera, stylus, tablet, touch screen,trackball, video camera, another suitable I/O device or a combination oftwo or more of these. An I/O device may include one or more sensors.This disclosure contemplates any suitable I/O devices and any suitableI/O interfaces 908 for them. Where appropriate, I/O interface 908 mayinclude one or more device or software drivers enabling processor 902 todrive one or more of these I/O devices. I/O interface 908 may includeone or more I/O interfaces 908, where appropriate. Although thisdisclosure describes and illustrates a particular I/O interface, thisdisclosure contemplates any suitable I/O interface.

In particular embodiments, communication interface 910 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 900 and one or more other computer systems 900 or one ormore networks. As an example and not by way of limitation, communicationinterface 910 may include a network interface controller (NIC) ornetwork adapter for communicating with an Ethernet or other wire-basednetwork or a wireless NIC (WNIC) or wireless adapter for communicatingwith a wireless network, such as a WI-FI network. This disclosurecontemplates any suitable network and any suitable communicationinterface 910 for it. As an example and not by way of limitation,computer system 900 may communicate with an ad hoc network, a personalarea network (PAN), a local area network (LAN), a wide area network(WAN), a metropolitan area network (MAN), or one or more portions of theInternet or a combination of two or more of these. One or more portionsof one or more of these networks may be wired or wireless. As anexample, computer system 900 may communicate with a wireless PAN (WPAN)(such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAXnetwork, a cellular telephone network (such as, for example, a GlobalSystem for Mobile Communications (GSM) network), or other suitablewireless network or a combination of two or more of these. Computersystem 900 may include any suitable communication interface 910 for anyof these networks, where appropriate. Communication interface 910 mayinclude one or more communication interfaces 910, where appropriate.Although this disclosure describes and illustrates a particularcommunication interface, this disclosure contemplates any suitablecommunication interface.

In particular embodiments, bus 912 includes hardware, software, or bothcoupling components of computer system 900 to each other. As an exampleand not by way of limitation, bus 912 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 912may include one or more buses 912, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Miscellaneous

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative.

What is claimed is:
 1. A method comprising, by one or more computingdevices: retrieving a first vector representation of a first entity thata user has interacted with, wherein the first vector representation isgenerated using an initial deep-learning model; retrieving a secondvector representation of a second entity that the user has notinteracted with, wherein the second vector representation is generatedusing the initial deep-learning model; determining a first similarityscore between the first vector representation and a vectorrepresentation of the user; determining a second similarity scorebetween the second vector representation and the vector representationof the user; upon determining that the second similarity score isgreater than the first similarity score, updating, using the initialdeep-learning model, the second vector representation; and generating anupdated deep-learning model based on the initial deep-learning model andthe updated second vector representation.
 2. The method of claim 1,wherein the retrieving of the first vector representation comprises:accessing a set of vector representations of entities that the user hasinteracted with, wherein the set comprises the first vectorrepresentation and a plurality of additional vector representations,each additional vector representation corresponding to a respectiveentity that the user has interacted with; selecting, from the set, thefirst vector representation; and removing the first vectorrepresentation from the set.
 3. The method of claim 2, wherein the firstvector representation is selected randomly from the set of vectorrepresentations.
 4. The method of claim 1, wherein retrieving the secondvector representation comprises: accessing a set comprising a pluralityof additional vector representations, each additional vectorrepresentation corresponding to a respective entity that the user hasnot interacted with, wherein the set further comprises the second vectorrepresentation; and selecting, from the set, the second vectorrepresentation and one or more additional vector representations.
 5. Themethod of claim 4, wherein a number of the selected one or moreadditional vector representations is based on a loss function of theinitial deep-learning model.
 6. The method of claim 4, furthercomprising: determining, for each additional vector representation, arespective similarity score between the additional vector representationand the vector representation of the user; assigning a respectiveranking to the first vector representation and to each additional vectorrepresentation based on the first similarity score and the respectivesimilarity scores; and updating one or more of the additional vectorrepresentations based on the respective rankings.
 7. The method of claim1, further comprising: retrieving a plurality of additional vectorrepresentations, each additional vector representation corresponding toa respective additional entity that the user has interacted with; andcombining the plurality of additional vector representations to generatethe vector representation of the user.
 8. The method of claim 1, whereinupdating the second vector representation comprises iteratively updatingthe second vector representation until the second similarity score isless than the first similarity score.
 9. The method of claim 1, furthercomprising: retrieving a third vector representation corresponding to athird entity that that the user has not interacted with; and predictinga relevance to the user of the third entity using the updateddeep-learning model.
 10. The method of claim 9, wherein the third vectorrepresentation is generated by the updated deep-learning model.
 11. Themethod of claim 1, further comprising accessing a multi-dimensionalembedding space, wherein the embedding space comprises: a user embeddingcorresponding to the vector representation of the user; a firstembedding corresponding to the first vector representation; a secondembedding corresponding to the second vector representation; and aplurality of additional embeddings, each additional embeddingcorresponding to a respective additional vector representation of anadditional entity.
 12. The method of claim 11, further comprisingidentifying a third entity that the user has not interacted with asrelevant to the user by applying a search algorithm to the embeddingspace, wherein the search algorithm determines that an embeddingcorresponding to a third vector representation of the third entity iswithin a threshold distance of the user embedding in the embeddingspace.
 13. The method of claim 12, further comprising: sending, to aclient system of the user, instructions for presenting the identifiedthird entity to the user.
 14. The method of claim 1, wherein the user isa user of a social-networking system, and wherein the method furthercomprises: accessing a social graph of the social-networking system, thesocial graph comprising a plurality of nodes and a plurality of edgesconnecting the nodes, each of the edges between two of the nodesrepresenting a single degree of separation between them, the nodescomprising: a first node corresponding to the user; and a plurality ofsecond nodes, each second node corresponding to a respective entity. 15.The method of claim 14, wherein the user has interacted with the firstentity via a social-networking action of the user, wherein thesocial-networking action is taken with respect to the first node and asecond node corresponding to the first entity.
 16. The method of claim15, wherein the social-networking action represents an expression ofaffinity for the first entity.
 17. The method of claim 1, wherein eitherone or both of the first entity or the second entity comprise a pagehosted by a social-networking system.
 18. The method of claim 1, whereinthe first vector representation, the second vector representation, andthe vector representation of the user each comprise a respectived-dimensional intensity vector.
 19. One or more computer-readablenon-transitory storage media embodying software that is operable whenexecuted to: retrieve a first vector representation of a first entitythat a user has interacted with, wherein the first vector representationis generated using an initial deep-learning model; retrieve a secondvector representation of a second entity that the user has notinteracted with, wherein the second vector representation is generatedusing the initial deep-learning model; determine a first similarityscore between the first vector representation and a vectorrepresentation of the user; determine a second similarity score betweenthe second vector representation and the vector representation of theuser; upon determining that the second similarity score is greater thanthe first similarity score, update, using the initial deep-learningmodel, the second vector representation; and generate an updateddeep-learning model based on the initial deep-learning model and theupdated second vector representation.
 20. A system comprising: one ormore processors; and a memory coupled to the processors comprisinginstructions executable by the processors, the processors operable whenexecuting the instructions to: retrieve a first vector representation ofa first entity that a user has interacted with, wherein the first vectorrepresentation is generated using an initial deep-learning model;retrieve a second vector representation of a second entity that the userhas not interacted with, wherein the second vector representation isgenerated using the initial deep-learning model; determine a firstsimilarity score between the first vector representation and a vectorrepresentation of the user; determine a second similarity score betweenthe second vector representation and the vector representation of theuser; upon determining that the second similarity score is greater thanthe first similarity score, update, using the initial deep-learningmodel, the second vector representation; and generate an updateddeep-learning model based on the initial deep-learning model and theupdated second vector representation.